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Optimization Techniques for Tree-Structured Nonlinear Problems

Jens Hübner (info***at***jhuebner.de)
Martin Schmidt (martin.schmidt***at***uni-trier.de)
Marc C. Steinbach (mcs***at***ifam.uni-hannover.de)

Abstract: Robust model predictive control approaches and other applications lead to nonlinear optimization problems defined on (scenario) trees. We present structure-preserving Quasi-Newton update formulas as well as structured inertia correction techniques that allow to solve these problems by interior-point methods with specialized KKT solvers for tree-structured optimization problems. The same type of KKT solvers could be used in active-set based SQP methods. The viability of our approach is demonstrated by two robust control problems.

Keywords: Nonlinear stochastic optimization, Interior-point methods, Structured Quasi-Newton updates, Structured inertia correction, Robust model predictive control

Category 1: Nonlinear Optimization

Category 2: Stochastic Programming


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Entry Submitted: 02/06/2017
Entry Accepted: 02/06/2017
Entry Last Modified: 05/28/2019

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